CN113821882B - Fan blade single-target optimization sequencing method based on triaxial moment - Google Patents
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Abstract
The invention provides a fan blade single-target optimization ordering method based on triaxial moment, which comprises the following steps: s 1, sequencing pretreatment is carried out by adopting sequencing software, equivalent synthetic moment is constructed, and an initial sequence is established; s 2, performing loop iteration operation by using a sensitive area search algorithm integrated in the sequencing software to obtain the optimal sequencing order of the equivalent unbalance. According to the invention, three moment values are synthesized into a single moment by constructing an equivalent synthetic moment concept and method, so that the multi-objective optimization problem is converted into single-objective optimization, the optimization problem processing is simplified, and the calculation is convenient. Meanwhile, through analysis of unbalanced effects generated by moments in different directions and combination of conversion between a fan rotor supporting structure and static/even unbalanced quantity, the axial moment of the blade is weighted, and the problem of moment synthesis weight distribution is solved.
Description
Technical Field
The invention relates to the field of engine assembly, in particular to a fan blade single-target optimization sequencing method based on triaxial moment.
Background
In large bypass ratio turbofan engines, the fan rotor blades are the main components that generate thrust, often being large in size, weight, and complex in shape, resulting in large differences in static torque. Because of equipment capacity and cost limitations, generally, fan blades are not balanced with the fan rotor, but are only installed after being sequenced by static torque during final assembly. The method for measuring and sequencing the moment of the blades has direct influence on the final balance state, and further has key influence on the vibration of the whole engine during the working process of the engine.
Sequencing accuracy and efficiency are important indicators for evaluating the level of fan blade sequencing technology. FIG. 1 is a schematic diagram of a three axis static moment of a fan blade according to the prior art. As shown in fig. 1, the fan blades in turbofan engines have moments, radial, tangential and axial, respectively, at an axis 20 through the center of gravity and parallel to the engine axis, a transverse axis 30 through the center of gravity, relative to the engine axis 10. Traditionally, however, only radial static moments are measured for narrow chord rotor blades of turbines and the like, and optimized for sequencing by a single objective. For the fan blade with complex shape and wide chord machine, not only the dispersion of the centroid position along the radial direction, but also the dispersion of the axial direction and the tangential direction (as shown in fig. 1) are considered.
In view of the above, a common method in the industry is to measure the triaxial moment of a large-sized fan blade by using a biaxial moment balance through a deflection fixture, and then perform double-objective optimized sequencing according to the static/even moment. The disadvantages of this ordering method are: the algorithm is complex and has low efficiency. And an inherent characteristic of multi-objective optimization is that there is no globally optimal solution, but a final solution is obtained, further processing is required, and inconvenience is brought to engineering practice.
In summary, a thorough solution is urgently needed at present, and not only can the three-axis moment sequencing be performed, but also the unique optimal sequencing result can be obtained efficiently and automatically. The optimized sequencing method of the wide-chord fan blades in the prior art mainly needs to solve the following problems:
1. The triaxial moment of the wide chord fan blade can generate two different unbalance effects of static/even, and there is no correlation between the static/even. If the optimization is performed by the double objective optimization function according to the following formula one, a unique optimal solution cannot be obtained. It is necessary to find a method to mechanically solve the selection of the triaxial moment optimization targets of the wide chord and large blade, and to convert the double-target optimization problem into a single-target optimization problem that can obtain an optimal solution, so as to simplify engineering application.
2. In converting the dual objective to single objective optimization, a method for effectively assigning the impact weight needs to be found.
3. The contradiction between the ordering calculation efficiency and the accuracy needs to be resolved in order to quickly obtain the optimal solution of the ordering.
Based on the above, the invention provides a fan blade single-target optimized sorting method based on triaxial moment, so as to solve the technical problems.
Disclosure of Invention
The invention aims to overcome the defects of complex algorithm, low efficiency and the like of triaxial moment in the prior art and provides a single-target optimized sorting method for fan blades based on triaxial moment.
The invention solves the technical problems by the following technical proposal:
The fan blade single-target optimized sorting method based on the triaxial moment is characterized by comprising the following steps of:
S 1, sequencing pretreatment is carried out by adopting sequencing software, equivalent synthetic moment is constructed, and an initial sequence is established;
S 2, performing loop iteration operation by using a sensitive area search algorithm integrated in the sequencing software to obtain the optimal sequencing order of the equivalent unbalance.
According to an embodiment of the present invention, the step S 1 specifically includes:
s 11, measuring and obtaining triaxial moment of the whole set of blades, sorting the triaxial moment into gauges Fan Geshi, and inputting the triaxial moment into sequencing software;
S 12, respectively synthesizing radial moment and tangential moment of all blades into static balance vectors Ms of the corresponding blades and an included angle alpha between the synthesized static vectors and the radial moment direction by a preprocessing algorithm of a corresponding module in the sequencing software;
S 13, determining the weight of the even unbalance amount under the worst condition according to the mass G and the span L of the fan rotor blade, and giving a weighting coefficient c;
s 14, synthesizing the synthesized static balance vector Ms of each blade and the corresponding axial moment MA into equivalent synthesized moment M' by a preprocessing algorithm of a corresponding module in the sequencing software;
S 15, establishing an initial sequence according to the equivalent synthetic moment of the blade.
According to one embodiment of the present invention, the included angle α between the static balance vector Ms and the resultant static vector and the radial moment direction in the step S 12 is as follows:
Where M R represents radial moment and M T represents tangential moment.
According to one embodiment of the present invention, the weighting coefficient c in the step S 13 satisfies:
where MA represents the axial moment, G represents the mass of the fan rotor blade, and L represents the span of the fan rotor blade.
According to one embodiment of the present invention, the equivalent resultant torque M' in the step S 14 satisfies:
According to one embodiment of the present invention, the step S 2 specifically includes the following steps:
S 21, calculating a composite vector of the equivalent unbalance amount, and taking the current sequence as an initial sequence. Let the number of leaves be n, take the important position leaf as i (i=1) leaf;
s 22, taking the i-th blade in the clockwise direction as a trial adjustment blade, sequentially and respectively adjusting positions (example pass iteration) with all the blades from the (i+1) -n in the clockwise direction, and respectively calculating the synthetic vector of the equivalent unbalance after adjustment;
s 23, screening out and recording a temporary sequence of the blade with the synthesized vector value closest to the target value after trial adjustment, namely the optimal adjustment sequence of the trial adjustment blades;
S 24, comparing the optimal adjustment sequence of the S 23 with the synthesized vector of the sequence before adjustment, and taking the sequence of the better result as a temporary optimal sequence;
S 25, judging whether all blades are used as trial adjustment blades (i=n), and if not, returning to the step S 22; if yes, ending the test adjustment of the round, and entering step 26;
S 26, judging whether new optimal adjustment is generated in the trial adjustment of the round, if so, taking the obtained optimal sequence as the new sequence after the iterative trial adjustment of the round, and repeatedly executing a new round of adjustment from the step S 21 to the step S 23;
If not, the blade sequence at this time accords with the optimal sequencing result of the following formula, and the optimization is stopped.
Where US represents the amount of static unbalance and Uc represents the amount of even unbalance.
According to an embodiment of the present invention, the vicinity of the key position in step S 21 is a sensitive area.
According to one embodiment of the present invention, the example pass iteration specifically includes: and (5) the trial adjustment blades are sequentially subjected to trial adjustment with the rest blades in the clockwise direction to install positions.
The invention has the positive progress effects that:
the fan blade single-target optimization sequencing method based on the triaxial moment has the following advantages:
1. by constructing the concept and method of equivalent synthetic moment, three moment values are synthesized into a single moment, the multi-objective optimization problem is converted into single-objective optimization, the optimization problem processing is simplified, and the calculation is convenient;
2. The axial moment of the blade is weighted by analyzing unbalanced effects generated by moments in different directions and combining conversion between a fan rotor supporting structure and static/even unbalanced quantity, so that the problem of moment synthesis weight distribution is solved;
3. By developing a sensitive area searching method which is more suitable for blade sequencing, the efficiency and the optimization precision are far higher than those of the traditional method, and the contradiction between the efficiency and the quality is solved.
Drawings
The above and other features, properties and advantages of the present invention will become more apparent from the following description of embodiments taken in conjunction with the accompanying drawings in which like reference characters designate like features throughout the drawings, and in which:
FIG. 1 is a schematic diagram of a three axis static moment of a fan blade according to the prior art.
FIG. 2 is a schematic diagram of an optimized sequence software interface in a fan blade single-target optimized sequence method based on triaxial moment in the present invention.
FIG. 3 is a schematic diagram of radial and tangential moments of a blade in a fan blade single-target optimized sequencing method based on triaxial moment according to the present invention.
Fig. 4 is a schematic diagram showing the synthesis of axial moments of blades in the fan blade single-target optimized sorting method based on triaxial moment.
FIG. 5 is a schematic diagram of the static unbalance and the even unbalance combined into equivalent unbalance in the fan blade single-target optimized sorting method based on triaxial moment according to the present invention.
FIG. 6 is a flow chart of a fan blade single target optimized sequencing method based on three axial moments of the present invention.
Detailed Description
In order to make the above objects, features and advantages of the present invention more comprehensible, embodiments accompanied with figures are described in detail below.
Embodiments of the present invention will now be described in detail with reference to the accompanying drawings. Reference will now be made in detail to the preferred embodiments of the present invention, examples of which are illustrated in the accompanying drawings. Wherever possible, the same reference numbers will be used throughout the drawings to refer to the same or like parts.
Furthermore, although terms used in the present invention are selected from publicly known and commonly used terms, some terms mentioned in the present specification may be selected by the applicant at his or her discretion, the detailed meanings of which are described in relevant parts of the description herein.
Furthermore, it is required that the present invention is understood, not simply by the actual terms used but by the meaning of each term lying within.
FIG. 2 is a schematic diagram of an optimized sequence software interface in a fan blade single-target optimized sequence method based on triaxial moment in the present invention. FIG. 3 is a schematic diagram of radial and tangential moments of a blade in a fan blade single-target optimized sequencing method based on triaxial moment according to the present invention. Fig. 4 is a schematic diagram showing the synthesis of axial moments of blades in the fan blade single-target optimized sorting method based on triaxial moment. FIG. 5 is a schematic diagram of the static unbalance and the even unbalance combined into equivalent unbalance in the fan blade single-target optimized sorting method based on triaxial moment according to the present invention. FIG. 6 is a flow chart of a fan blade single target optimized sequencing method based on three axial moments of the present invention.
As shown in fig. 2 to 6, the invention discloses a fan blade single-target optimized sorting method based on triaxial moment, which comprises the following steps:
and S 1, adopting sequencing software to perform sequencing pretreatment, constructing equivalent synthetic moment, and establishing an initial sequence.
Wherein, the step S 1 preferably includes:
s 11, measuring and obtaining triaxial moment of the whole set of blades, sorting the triaxial moment into gauges Fan Geshi, and inputting the triaxial moment into sequencing software (shown in FIG. 2).
The optimized sequencing software interface in the fan blade single-target optimized sequencing method based on the triaxial moment is shown in fig. 2, the radial moment interface is shown here, the radial moment obtained through input measurement is processed by corresponding modules in sequencing software, and the values of the synthesized radial moment, the synthesized static vector, the synthesized even vector, the synthesized equivalent moment, the corresponding angle and the like are obtained, and can be sequenced through the software.
S 12, respectively synthesizing radial moment and tangential moment of all blades into static balance vectors Ms of the corresponding blades and an included angle alpha between the synthesized static vectors and the radial moment direction by a preprocessing algorithm of a corresponding module in the sequencing software (shown in figures 3 and 4).
The radial moment and tangential moment of the blades in the single-target optimized fan blade sorting method based on triaxial moment of the present invention are shown in fig. 3. Taking the fan blades 110 on the fan disk 100 as an example, the radial moment M R and the tangential moment M T of the radial reference shaft 40 are synthesized into a static balance vector Ms (located in the direction of the blade centroid 120) of the corresponding blade and an included angle α of the synthesized static vector and the radial moment direction.
Preferably, in the step S 12, the included angle α between the static balance vector Ms and the combined static vector and the radial moment direction satisfies:
Where M R represents radial moment and M T represents tangential moment.
S 13, determining the weight of the even unbalance amount under the worst condition according to the mass G and the span L of the fan rotor blade, and giving a weighting coefficient c.
Preferably, the weighting coefficient c in the step S 13 satisfies:
Where M A represents the axial moment, G represents the mass of the fan rotor blade, and L represents the span of the fan rotor blade (as shown in FIG. 4).
FIG. 4 illustrates the synthesis of axial moments of blades in a single-objective optimized fan blade ordering method based on triaxial moments according to the present invention. Taking the point a on the fan blade 110 as an example, M A represents the axial moment of the point a along the axial direction, and the weighting coefficient c is obtained through calculation of the axial moment value.
S 14, synthesizing the synthetic static balance vector Ms of each blade and the corresponding axial moment M A into equivalent synthetic moment M' (shown in figures 3 and 4) by a preprocessing algorithm of a corresponding module in the sequencing software.
Preferably, the equivalent resultant moment M' in the step S 14 satisfies:
S 15, establishing an initial sequence according to the equivalent synthetic moment of the blade.
And step 2, performing loop iteration operation by using a sensitive area search algorithm (shown in the flow chart of fig. 6) integrated in the ordering software to obtain the optimal ordering sequence of the equivalent unbalance amount.
The step S 2 preferably includes the steps of:
S 21, calculating a composite vector of the equivalent unbalance according to the following formula, and taking the current sequence as an initial sequence. The number of the blades is set as n, the blade at the key position is taken as a blade with the number of i (i=1), namely the blade at the key position (sensitive area) is taken as a blade with the number of 1, and the rest blades are numbered clockwise; the following formula is satisfied: Or (b)
Where U S represents the static unbalance amount, and Uc represents the even unbalance amount (as shown in fig. 5).
FIG. 5 illustrates the static unbalance and even unbalance combined into equivalent unbalance in the triaxial moment based fan blade single-objective optimized sorting method of the present invention. The static unbalance amount U S and the even unbalance amount U C are synthesized as a synthesized vector U' of the equivalent unbalance amount.
The vicinity of the key position in step S 21 is preferably a sensitive area. Since adjustment of the blade position in the region near the focus of the composite vector greatly affects the composite vector, the region near the focus blade is referred to as a sensitive region.
S 22, taking the i-th blade in the clockwise direction as a trial adjustment blade, sequentially and respectively adjusting positions (example pass iteration) with all the blades in the (i+1) -n-th clockwise direction, and respectively calculating the synthesized vector of the equivalent unbalance after adjustment.
Specifically, in step S 22, the sensitive area search process is started: starting from the sensitive area, taking all clockwise blades as trial adjustment blades, and respectively calculating the synthetic vector of the equivalent unbalance after adjustment by each trial adjustment blade and the rest clockwise blades in sequence for position adjustment (e.g. iteration).
The example pass iterations described herein preferably include, in particular: and (5) the trial adjustment blades are sequentially subjected to trial adjustment with the rest blades in the clockwise direction to install positions.
Thereafter, the resulting vector obtained in step S 22 is compared with the resulting vector value before the trial adjustment, which will yield a sequence of better results.
S 23, screening to obtain and record a temporary sequence of the blade with the synthesized vector value closest to the target value after trial adjustment, namely, the optimal adjustment sequence of the trial adjustment blades, namely, screening and recording the optimal adjustment of the trial adjustment blades; .
S 24 comparing the optimal adjustment sequence of S 23 with the synthesized vector of the sequence before adjustment, and taking the sequence of the better result as the temporary optimal sequence.
S 25, judging whether all blades are used as trial adjustment blades (i=n), and if not, returning to the step S 22; if yes, the test adjustment is ended, and the step S 26 is entered.
S 26, judging whether new optimal adjustment is generated in the trial adjustment of the round, if so, taking the obtained optimal sequence as the new sequence after the iterative trial adjustment of the round, and repeatedly executing a new round of adjustment from the step S 21 to the step S 23;
If not, the blade sequence at this time accords with the optimal sequencing result of the following formula, and the optimization is stopped.
Wherein U S represents the static unbalance amount and Uc represents the even unbalance amount.
In summary, the fan blade single-target optimized sorting method based on the triaxial moment has the following advantages:
1. by constructing the concept and method of equivalent synthetic moment, three moment values are synthesized into a single moment, the multi-objective optimization problem is converted into single-objective optimization, the optimization problem processing is simplified, and the calculation is convenient;
2. The axial moment of the blade is weighted by analyzing unbalanced effects generated by moments in different directions and combining conversion between a fan rotor supporting structure and static/even unbalanced quantity, so that the problem of moment synthesis weight distribution is solved;
3. by developing a sensitive area searching method which is more suitable for blade sequencing, the efficiency and the optimization precision are far higher than those of the traditional method, and the contradiction between the efficiency and the quality is solved. Since adjustment of the blade position in the region near the focus of the composite vector greatly affects the composite vector, the region near the focus blade is referred to as a sensitive region. The sensitive area search method is a search method starting from a sensitive area.
While specific embodiments of the invention have been described above, it will be appreciated by those skilled in the art that these are by way of example only, and the scope of the invention is defined by the appended claims. Various changes and modifications to these embodiments may be made by those skilled in the art without departing from the principles and spirit of the invention, but such changes and modifications fall within the scope of the invention.
Claims (6)
1. The fan blade single-target optimized sequencing method based on the triaxial moment is used for engine assembly and is characterized by comprising the following steps of:
S 1, sequencing pretreatment is carried out by adopting sequencing software, equivalent synthetic moment is constructed, and an initial sequence is established;
S 2, performing loop iteration operation by a sensitive area search algorithm integrated in the sequencing software to obtain an optimal sequencing order of the equivalent unbalance;
The step S 1 specifically includes:
s 11, measuring and obtaining triaxial moment of the whole set of blades, sorting the triaxial moment into gauges Fan Geshi, and inputting the triaxial moment into sequencing software;
S 12, respectively synthesizing radial moment and tangential moment of all blades into a static balance vector M S of the corresponding blade and synthesizing an included angle alpha between the static vector and the radial moment direction by a preprocessing algorithm of a corresponding module in the sequencing software;
S 13, determining the weight of the even unbalance amount under the worst condition according to the mass G and the span L of the fan rotor blade, and giving a weighting coefficient c;
S 14, synthesizing a synthetic static balance vector M S of each blade and a corresponding axial moment M A into an equivalent synthetic moment M' by a preprocessing algorithm of a corresponding module in the sequencing software;
S 15, establishing an initial sequence according to the equivalent synthetic moment of the blade;
The step S 2 specifically includes the following steps:
s 21, calculating a synthetic vector of the equivalent unbalance, and taking the current sequence as an initial sequence; let the number of leaves be n, take the important position leaf as i number leaf, i=1;
S 22, taking the i-th blade in the clockwise direction as a trial adjustment blade, sequentially and respectively exchanging positions with all the i+1-th blades in the clockwise direction, performing iteration for example, and respectively calculating the synthetic vector of the exchanged equivalent unbalance;
s 23, screening out and recording a temporary sequence of the blade with the synthesized vector value closest to the target value after trial adjustment, namely the optimal adjustment sequence of the trial adjustment blades;
S 24, comparing the optimal adjustment sequence of the S 23 with the synthesized vector of the sequence before adjustment, and taking the sequence of the better result as a temporary optimal sequence;
S 25, judging whether all the blades are used as trial adjustment blades, wherein i=n, and if not, returning to the step S 22; if yes, ending the test adjustment of the round, and entering step 26;
S 26, judging whether new optimal adjustment is generated in the trial adjustment of the round, if so, taking the obtained optimal sequence as the new sequence after the iterative trial adjustment of the round, and repeatedly executing a new round of adjustment from the step S 21 to the step S 23;
If not, the blade sequence at the moment accords with the optimal sequencing result of the following formula, and the optimization is stopped;
Wherein U S represents the static unbalance amount, and U C represents the even unbalance amount.
2. The method for optimized single-target sequencing of fan blades based on three-axis torque as set forth in claim 1, wherein the static balance vector M S and the angle α between the resultant static vector and the radial torque direction in step S 12 satisfy:
Where M R represents radial moment and M T represents tangential moment.
3. The method for optimized single-objective sequencing of fan blades based on three-axis moments according to claim 2, wherein said weighting coefficient c in step S 13 satisfies:
where M A represents the axial moment, G represents the mass of the fan rotor blade, and L represents the span of the fan rotor blade.
4. The method for optimized single-target sequencing of fan blades based on three-axis torque as set forth in claim 3, wherein said equivalent resultant torque M' in said step S 14 satisfies:
5. The method for optimized single-target sequencing of fan blades based on three-axis torque as recited in claim 1, wherein a sensitive area is near the focal position in said step S 21.
6. The method for optimizing and sequencing fan blades based on triaxial moment according to claim 1, wherein the example pass iteration specifically includes: and (5) the trial adjustment blades are sequentially subjected to trial adjustment with the rest blades in the clockwise direction to install positions.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105320792A (en) * | 2014-07-30 | 2016-02-10 | 南车株洲电力机车研究所有限公司 | Method for solving unbalance fault of impellers of wind turbine generator |
| CN108121877A (en) * | 2018-01-05 | 2018-06-05 | 扬州大学 | A kind of full operating condition sharf method for optimizing position of water pump |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20160130949A1 (en) * | 2012-01-31 | 2016-05-12 | United Technologies Corporation | Low noise turbine for geared turbofan engine |
| FR3037168A1 (en) * | 2015-06-08 | 2016-12-09 | Snecma | METHOD FOR SIMULATION OF AUB DISTRIBUTION ON A TURBOMACHINE DISK |
| CN204855091U (en) * | 2015-07-17 | 2015-12-09 | 中国航空工业集团公司沈阳发动机设计研究所 | Anchor clamps are measured to ARC blade weight square |
| FR3059799B1 (en) * | 2016-12-07 | 2022-06-10 | Safran Aircraft Engines | METHOD FOR SIMULATING BLADE DISTRIBUTION ON A TURBOMACHINE DISC |
| CN110889244B (en) * | 2019-12-20 | 2024-05-10 | 哈尔滨工业大学 | Sequencing method of large-sized high-speed rotation equipment blades based on mass moment minimization |
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| CN105320792A (en) * | 2014-07-30 | 2016-02-10 | 南车株洲电力机车研究所有限公司 | Method for solving unbalance fault of impellers of wind turbine generator |
| CN108121877A (en) * | 2018-01-05 | 2018-06-05 | 扬州大学 | A kind of full operating condition sharf method for optimizing position of water pump |
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